PoS(EPS-HEP2017)792 https://pos.sissa.it/ ∗ † [email protected] Speaker. on behalf of the ATLAS Collaboration Producing the large samples of simulated eventsies required with by the many physics ATLAS detector and using performance the stud- fullFast GEANT4 simulation detector tools simulation are is a highly CPU usefulsimulations intensive. way are of not reducing needed. the During Run CPU 1simulation requirements of when (FastCaloSim) the detailed was Large detector Hadron successfully Collider used (LHC), inof a fast the ATLAS. FastCaloSim calorimeter particle provides energy a response simulation attailed the particle calorimeter shower read-out shapes cell and level, thecalorimeter taking layers. correlations into It between is account the interfaced the to energy the de- depositions standardand ATLAS digitisation in it and the can reconstruction software, various be tuned tois data in more development, easily incorporating than the Geant4.FastCaloSim experience aims Now with to an the improved overcome version version some usedtion of limitations FastCaloSim during of of Run substructure the 1. variables first forcalorimeters, version The boosted which by new is jets, improving important and the for giving descrip- tial forward a jets prototype better in is vector performance available boson and inFastCaloSim fusion is the parametrisation topologies. being to forward tested simulate A several and first billion validated par- events now. in the ATLAS upcoming plans LHC to runs. use this new † ∗ Copyright owned by the author(s) under the terms of the Creative Commons c Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). EPS-HEP 2017, European Physical Society conference5-12 on July High 2017 Energy Physics Venice, Italy Matthew Heath University of Edinburgh (GB) E-mail: The New ATLAS Fast Calorimeter Simulation PoS(EPS-HEP2017)792 8). . and 475) 0 . 3 1 | ≤ η | ≤ | η | Matthew Heath ], simulated Monte 2 ]. ]. As the luminosity of 1 4 7) and a larger barrel ( ] at the LHC [ . 1 1 | ≤ η ≤ | 8 . 2). It is a liquid-argon (LAr) detector with zig- . 1 3 . 6 | ≤ η ≤ | 375 . A schematic of the ATLAS calorimeter system [ ] which models a detailed description of particles as they interact with 3 will give an overview of the structure of the ATLAS calorimeter, with a Figure 1: 2 . The electromagnetic calorimeter is divided into a barrel section (EMB, 1 , followed by a summary in Section 5 The calorimeter system of the ATLAS detector is shown and labelled with its principal layers To run successful data analyses for the ATLAS experiment [ respectively. The current state of the package along with steps to validate it are presented in Section in Figure Carlo event samples areand required their to interactions accurately represent withwith both the the the detector. Geant4 package physical [ The processes full of simulation interest of the ATLAS detector is handled summary of legacy FastCaloSim and the4 improvements included in the upgrade in Sections 2. The ATLAS Calorimeter and an end-cap section (EMEC,zagging 1 accordion shaped electrodes and leadcomposed absorber of plates. two The small hadronic extended tile barrel barrel cylinders (TileCal) (0 is the materials in thethe full CPU detector time geometry. requirementspent of However simulating several the the minutes development disadvantage of per of particle event, showers this in in level the particular of calorimeters about [ detail 90% is of this time is New Fast Calorimeter Simulation 1. Introduction the LHC increases, this simulation timeevents requirement with becomes sufficiently increasingly large challenging to statistics.was produce developed to The provide ATLAS a Fast parametrised simulation Calorimeterin of the Simulation the particle (FastCaloSim) energy calorimeter, response reducing and distribution thehow simulation an time improved version by of anoperation. FastCaloSim order is of Section being magnitude. developed, This describing article its outlines major principles of PoS(EPS-HEP2017)792 Matthew Heath 2) overlaps with TileCal, . 3 | ≤ η ≤ | 5 . ]. 1 2 ]. 5 9) uses, on each end-cap, an innermost module with copper absorber optimised . 4 single particle events. To further simplify the model, only photons and electrons are used | ≤ 6 η | 10 The single particle events are generated in a fine grid of energy and pseudorapidity, then when For the FastCaloSim upgrade, the general concept of the original package is followed but is For the lateral shower shape, the parametrisation describes the average shower shape for a Legacy FastCaloSim was developed to speed up the time spent simulating each event, while × 30 simulating a particle in the calorimeter theand closest the matching response parametrisation is is randomly loaded sampled intoergy from deposition memory that. per calorimeter Shower layer development and is lateral split shapeLegacy into of FastCaloSim longitudinal the uses energy en- density a around longitudinal the showertions parametrisation axis. in that reproduces shower development, fluctuations but andfluctuations. only correla- This average renders lateral the shower fast properties simulationventing unable and use to in uncorrelated describe analyses energy hadronic involving jet shower substructure. sub-clusters,allow pre- It it is to the describe objective of sub-clustersphysics the while lists FastCaloSim also upgrade from to Run incorporating 1 developments data. of updated geometry and 4. ATLAS Fast Calorimeter Simulation Upgrade implemented using machine learning techniques.are Pion, run photon, and in electron Geant4 single usingthe particle the longitudinal events latest energy parametrisation, ATLAS geometry a Principal andthe Component used correlated Analysis fractions to (PCA) of create is the used the totalcomponents. to parametrisations. energy convert deposited A For per neural layer network intothe is an amount then uncorrelated set used of of to data principal approximateperformed, required uncorrelated the to random cumulative numbers be histograms are to stored generatedenergy reduce and for to used be the to deposited parametrisations. determine per the layer fraction using When of the the total PCA weights simulation as step thegiven particle is parametrisation type inputs. in each calorimeterponent layer, as determined generated for by each the bin PCA. ofradially This the is symmetric leading binned principal shower in com- centre. coordinates of Thiswhich radius is random from numbers normalised and angle are and around used used the to as determine a the probability position distribution of from energy deposits for each layer. The for the parametrisation and simulationlieu of of electromagnetic all showers, hadrons and [ charged pions are used in still reproducing the key features of reconstructeding particle the properties. calorimeter This geometry is by managed modelling byparticle the simplify- showers cells with as a cuboids, parametrised then∼ replacing calorimeter the response development based of on the full Geant4 simulation of New Fast Calorimeter Simulation TileCal is a sampling calorimeter comprising oftiles alternating as layers the of sampling steel medium. absorber and The scintillating hadronic end-cap (HEC, 1 3. Legacy ATLAS Fast Calorimeter Simulation but uses LAr as the(FCal, active medium and copperfor plate electromagnetic interactions, absorbers. and then Finallyshowers, two the the other FCal forward modules also calorimeter with uses tungsten LAr as absorber the for active hadronic medium [ PoS(EPS-HEP2017)792 Matthew Heath 3 show the performance of the FastCaloSim upgrade in com- 2 25, so further development is important. . 0 < η < 20 . The left plot shows the number of sub-clusters produced per event while the right plot shows the FastCaloSim was developed as a parametrised calorimeter response in order to generate Monte The two plots shown in Figure In addition to the revised shower parametrisation, the exact geometry of the forward calorime- There is currently a functional prototype of the new FastCaloSim that can be used for single Carlo events at a muchis higher implementing rate methods than to Geant4. improve the Theprototype simulation upgrade has of been to particle produced. FastCaloSim shower Efforts under substructure to development legacy and improve FastCaloSim a this are prototype working underway. after validation against Geant4 and parison with the legacy packageof and clusters with from full the Geant4 upgrade simulation.simulating prototype is with It in greater can better fluctuations. be agreement There seenposited with by is that Geant4 photons also due the in an the to number electromagnetic improvement the barrel inenergy lateral due deposition. to determining shape an the The enhanced package energy parametrisation of is de- longitudinal a not range complete of however 0 and can only simulate single particles in 6. Summary Figure 2: reconstructed energy of photons in the thirdthe layer legacy of package the is EMB. labelled G4FastCalo as denotes ATLFASTII, results while from Geant4 the is upgrade, shown as FullG4. ter is added in place of theof simplified energy model, deposits and corrections in are the implemented LAr in calorimetersof the by the cell using assignment electrodes a into ‘wiggle consideration. function’ to take the accordion shape 5. Progress and Validation particles and is under validation. Plotsshowers are to produced assess comparing the properties upgrade’s of performance the against simulated the particle legacy package and with Geant4. New Fast Calorimeter Simulation lower the number of energyleads deposits to per a layer, clustered the structure more whenstructure reconstruction localised that is was and missing performed. irregular from This the legacy gives shape, FastCaloSim. rise which to the shower sub- PoS(EPS-HEP2017)792 3 70 (2003) 506 Matthew Heath A JINST , Eur. Phys. J. C , Nucl. Instrum. Meth. (2008) S08001 3 4 JINST , The ATLAS Experiment at the Large Hadron Collider The ATLAS Simulation Infrastructure The simulation principle and performance of the ATLAS fast calorimeter ATL-PHYS-PUB-2010-013 LHC Machine , GEANT4: A Simulation toolkit (2008) S08003 simulation FastCaloSim (2010) 823-874 [1] The ATLAS Collaboration, [2] L. Evens and P. Bryant, [3] S. Agostinelli et al., [4] The ATLAS Collaboration, [5] The ATLAS Collaboration, New Fast Calorimeter Simulation References